Quick Auction Notes This auction is eligible for combined shipping. Everything has been adult owned and comes from a pet & smoke free home. Toys come complete with all accessories, paperwork and packaging. You will not be disappointed with the condition of the figures. They have been stored away safely in storage, never opened or displayed. Toys have never been removed from the packaging. Not to mention, rarity aside, these awesome combiners just look cool packaged in a giftset like this! Packaging does contain some shelf wear/imperfections, so this auction is geared towards buyers looking to buy this item for opening, not for grading purposes. They also applied cartoon/anime accurate paint schemes on them as opposed to Hasbro's releases. The Buildron/Constructicon Maximus giftset was the rarest of the three combiners since it was released in limited numbers exclusively at Toys R Us stores in Japan, which made importing it even more difficult since you had to buy it off of a resident in Japan. The combiners were released only as giftsets by Takara in Japan and were done so in limited numbers near the end of the Superlink toyline. Item Description The following auction is for a new, mint in sealed box (MISB) Transformers Takara Superlink Energon EX-01 Buildron Constructicon Maximus Giftset TRU Japan Exclusive. only auction, any emails asking for an exception will be ignored. Model_output = self.Transformers Takara Superlink Energon EX-01 Buildron Constructicon Maximus Giftset TRU Japan Exclusive Sorry, but this is a Continental U.S. # Pass the decoder output through a final dense layer # Feed the encoder output into the decoderĭecoder_output = coder(decoder_input, encoder_output, dec_in_lookahead_mask, enc_padding_mask, training) # Create and combine padding and look-ahead masks to be fed into the decoderĭec_in_padding_mask = self.padding_mask(decoder_input)ĭec_in_lookahead_mask = self.lookahead_mask(decoder_input.shape)ĭec_in_lookahead_mask = maximum(dec_in_padding_mask, dec_in_lookahead_mask)Įncoder_output = self.encoder(encoder_input, enc_padding_mask, training) # Create padding mask to mask the encoder inputs and the encoder outputs in the decoderĮnc_padding_mask = self.padding_mask(encoder_input) Mask = 1 - linalg.band_part(ones((shape, shape)), -1, 0)ĭef call(self, encoder_input, decoder_input, training): # Mask out future entries by marking them with a 1.0 # of the attention weights that it will be masking later on # The shape of the mask should be broadcastable to the shape # Create mask which marks the zero padding values in the input by a 1.0 Self.model_last_layer = Dense(dec_vocab_size) coder = Decoder(dec_vocab_size, dec_seq_length, h, d_k, d_v, d_model, d_ff_inner, n, rate) Self.encoder = Encoder(enc_vocab_size, enc_seq_length, h, d_k, d_v, d_model, d_ff_inner, n, rate) Let’s create the following function to generate a padding mask for both the encoder and decoder:įrom tensorflow import math, cast, float32, linalg, ones, maximum, newaxisįrom import Denseĭef _init_(self, enc_vocab_size, dec_vocab_size, enc_seq_length, dec_seq_length, h, d_k, d_v, d_model, d_ff_inner, n, rate, **kwargs): The importance of having a padding mask is to make sure that these zero values are not processed along with the actual input values by both the encoder and decoder. You should already be familiar with the importance of masking the input values before feeding them into the encoder and decoder.Īs you will see when you proceed to train the Transformer model, the input sequences fed into the encoder and decoder will first be zero-padded up to a specific sequence length. Let’s start first by discovering how to apply masking. In this tutorial, you will join the two into a complete Transformer model and apply padding and look-ahead masking to the input values. You have seen how to implement the Transformer encoder and decoder separately. In generating an output sequence, the Transformer does not rely on recurrence and convolutions. The encoder-decoder structure of the Transformer architecture
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